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| 0 | 2 | 1443 | 1346 |
| 1 | 3 | 694 | 577 |
| 2 | 4 | 455 | 337 |
| 3 | 5 | 353 | 208 |
| 4 | 6 | 272 | 149 |
Your Business Model is Your Data Generating Process
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| dantegates.github.io | |
Disambiguating an overloaded term:
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Goal: Align these |
Stan Case Study: Golf putting
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| 0 | 2 | 1443 | 1346 |
| 1 | 3 | 694 | 577 |
| 2 | 4 | 455 | 337 |
| 3 | 5 | 353 | 208 |
| 4 | 6 | 272 | 149 |
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| Photo Credit: insidescience.org |
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Image Source: Stan Development Team |
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Image Source: Stan Development Team |
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Summary
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Image Source: Stan Development Team |
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Modeled outcome Will the ball go in the hole? -not- y=0/1? |
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Image Source: Stan Development Team |
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Features of model
Relationships
Constraints
Advantages over vanilla ML
Critiques
N changing over timewith pm.Model as model:
...
# R: learned parameter of recovery, e.g. pm.Beta()
# D_T: number of loans that defaulted `t-T` days ago
# PD_T: number of loans in a past-due state `t-T` days ago
# N: total number of loans
R = ...
D_t = (D_T + (1-R) * PD_T) / N
D = D_t / some_cdf(t-T)
# ↓↓↓ everything else same as before ↓↓↓
...Priors! All Bayesian models are DGPs!
Priors! All Bayesian models are DGPs!
From Wikipedia,
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